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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
TLDR
Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation. Expand
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
TLDR
This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods. Expand
Social IQA: Commonsense Reasoning about Social Interactions
TLDR
Social IQa is introduced, the first largescale benchmark for commonsense reasoning about social situations, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Expand
The Risk of Racial Bias in Hate Speech Detection
TLDR
This work proposes *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive. Expand
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
TLDR
Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level. Expand
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
TLDR
It is demonstrated how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts. Expand
Developing Age and Gender Predictive Lexica over Social Media
TLDR
Predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels achieve state-of-the-art accuracy. Expand
Towards Assessing Changes in Degree of Depression through Facebook
TLDR
A regression model is developed that predicts users’ degree of depression based on their Facebook status updates, and shows the potential to study factors driving individuals’ level of depression by looking at its most highly correlated language features. Expand
The role of personality, age, and gender in tweeting about mental illness
TLDR
Language-derived personality and demographic estimates show surprisingly strong performance in distinguishing users that tweet a diagnosis of depression or PTSD from random controls, reaching an area under the receiveroperating characteristic curve ‐ AUC ‐ of around .8 in all the authors' binary classification tasks. Expand
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
TLDR
It is shown how variants of the same writing task can lead to measurable differences in writing style, and a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases. Expand
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